Unified platform for monitoring and control of blood glucose levels in diabetic patients

ABSTRACT

A flexible system capable of utilizing data from different monitoring techniques and capable of providing assistance to patients with diabetes at several scalable levels, ranging from advice about long-term trends and prognosis to real-time automated closed-loop control (artificial pancreas). These scalable monitoring and treatment strategies are delivered by a unified system called the Diabetes Assistant (DiAs) platform. The system provides a foundation for implementation of various monitoring, advisory, and automated diabetes treatment algorithms or methods. The DiAs recommendations are tailored to the specifics of an individual patient, and to the patient risk assessment at any given moment.

GOVERNMENT RIGHTS

This invention was made with government support under Grant No. DK085623awarded by The National Institutes of Health. The government has certainrights in the invention.

BACKGROUND OF THE INVENTION

Diabetes mellitus (DM), often simply referred to as diabetes, is a groupof metabolic diseases characterized by high glucose levels in the blood(i.e. hyperglycemia), either because the body does not produce enoughinsulin (Type 1 DM or T1DM), or because cells do not respond to theinsulin that is produced (Type 2 DM or T2DM). Intensive treatment withinsulin and with oral medications to maintain nearly normal levels ofglycemia (i.e. euglycemia) markedly reduces chronic complications inboth T1DM and T2DM [1,2,3], but may risk symptomatic hypoglycemia andpotentially life-threatening severe hypoglycemia. Therefore,hypoglycemia has been identified as the primary barrier to optimaldiabetes management [4,5]. People with T1DM and T2DM face a lifelongoptimization problem: to maintain strict glycemic control withoutincreasing their risk for hypoglycemia. However, the struggle for closeglycemic control could result in large blood glucose (BG) fluctuationsover time. This process is influenced by many external factors,including the timing and amount of insulin injected, food eaten,physical activity, etc. In other words, BG fluctuations in diabetes arethe measurable result of the interactions of a complex and dynamicbiological system, influenced by many internal and external factors.

The optimization of this system depends largely on self-treatmentbehavior, which has to be informed by glucose monitoring and has toutilize data and technology available in the field. The currentlyaccessible data sources include self-monitoring of blood glucose (SMBG),continuous glucose monitoring (CGM), as well as assessment of symptomsand self-treatment practices. The available treatments includemedication (exclusively for T2DM), multiple daily insulin injections(MDI), and insulin pumps (CSII—continuous subcutaneous insulininjection). Currently, these treatments are at various stages ofdevelopment and clinical acceptance, with SMBG now a routine practice,CGM rapidly developing, and emerging integrated systems that combine CGMwith CSII and pave the way for the artificial pancreas of the nearfuture.

Self-Monitoring of Blood Glucose

Contemporary home BG meters offer convenient means for frequent andaccurate BG determinations through SMBG [6,7]. Most meters are capableof storing BG readings (typically over 150 readings) and have interfacesto download these readings into a computing device such a PC. The metersare usually accompanied by software that has capabilities for basic dataanalysis (e.g. calculation of mean BG, estimates of the average BG overthe previous two weeks, percentages in target, hypoglycemic andhyperglycemic zones, etc.), logging of the data, and graphicalrepresentations of the BG data (e.g. histograms, pie charts, etc.). In aseries of studies we have shown that specific risk analysis of SMBG datacould also capture long-term trends towards increased risk forhypoglycemia [8, 9,10], and could identify 24-hour periods of increasedrisk for hypoglycemia [11,12]. The basics of the risk analysis arepresented below. The methods outlined here have been applied to bothSMBG and CGM data.

Evaluating Risk for Hypoglycemia and Hyperglycemia:

These methods are based on the concept of Risk Analysis of BG data [13],and on the recognition of a specific asymmetry of the BG measurementscale that can be corrected by a mathematical data transformation [14].The risk analysis steps are as follows:

1. Symmetrization of the BG Scale:

A nonlinear transformation is applied to the BG measurements scale tomap the entire BG range (20 to 600 mg/dl, or 1.1 to 33.3 mmol/1) to asymmetric interval. The BG value of 112.5 mg/dl (6.25 mmol/1) is mappedto zero, corresponding to zero risk for hypo- or hyperglycemia. Theanalytical form of this transformation is f(BG, α,β)=[(ln(BG))^(α)−β],α, β>0, where the parameters are estimated a-s α=1.084, β=5.381,γ=1.509, if BG is measured in mg/dl and α=1.026, β=1.861, γ=1.794 if BGis measured in mmol/l [14].

2. Assignment of a Risk Value to Each SMBG Reading:

We define the quadratic risk function r(BG)=10f(BG)². The function r(BG)ranges from 0 to 100. Its minimum value is achieved at BG=112.5 mg/dl (asafe euglycemic BG reading), while its maximum is reached at the extremeends of the BG scale. Thus, r(BG) can be interpreted as a measure of therisk associated with a certain BG level. The left branch of thisparabola identifies the risk of hypoglycemia, while the right branchidentifies the risk of hyperglycemia.

3. Computing Measures of Risk for Hypoglycemia and Glucose Variability:

Let x₁, x₂, . . . x_(n) be a series of n BG readings, and letrl(BG)=r(BG) if f(BG)<0 and 0 otherwise; rh(BG)=r(BG) if f(BG)>0 and 0otherwise. Then the Low Blood Glucose Index (LBGI) is computed as:

${LBGI} = {\frac{1}{n}{\sum\limits_{i = 1}^{n}\;{{rl}\left( x_{i} \right)}}}$

In other words, the LBGI is a non-negative quantity that increases whenthe number and/or extent of low BG readings increases. In studies, theLBGI typically accounted for 40-55% of the variance of futuresignificant hypoglycemia in the subsequent 3-6 months [8,9,10], whichmade it a potent predictor of hypoglycemia based on SMBG. Similarly, wecompute the High Blood Glucose Index (HBGI) as follows:

${HBGI} = {\frac{1}{n}{\sum\limits_{i = 1}^{n}\;{{rh}\left( x_{i} \right)}}}$

The HBGI is a non-negative quantity that increases when the numberand/or extent of high BG readings increases.

Continuous Glucose Monitoring

Since the advent of continuous glucose monitoring technology 10 yearsago [15,16,17], which initially had limited performance particularly inthe hypoglycemic range [18,19], significant progress has been madetowards versatile and reliable CGM devices that not only monitor theentire course of BG day and night, but also provide feedback to thepatient, such as alarms when BG reaches preset low or high levels. Anumber of studies have documented the benefits of continuous glucosemonitoring [20,21,22,23] and charted guidelines for clinical use and itsfuture as a precursor to closed-loop control [24,25,26,27]. However,while CGM has the potential to revolutionize the control of diabetes, italso generates data streams that are both voluminous and complex. Theutilization of such data requires an understanding of the physical,biochemical, and mathematical principles and properties involved in thisnew technology. It is important to know that CGM devices measure glucoseconcentration in a different compartment—the interstitium. Interstitialglucose (IG) fluctuations are related to BG presumably via the diffusionprocess [28,29,30]. To account for the gradient between BG and IG, CGMdevices are calibrated with capillary glucose, which brings thetypically lower IG concentration to corresponding BG levels. Successfulcalibration would adjust the amplitude of IG fluctuations with respectto BG, but would not eliminate the possible time lag due to BG-to-IGglucose transport and the sensor processing time (instrument delay).Because such a time lag could greatly influence the accuracy of CGM, anumber of studies were dedicated to its investigation, yielding variousresults [31,32,33,34]. For example, it was hypothesized that if glucosefall is due to peripheral glucose consumption the physiologic time lagwould be negative, i.e. fall in IG would precede fall in BG [28,35]. Inmost studies IG lagged behind BG (most of the time) by 4-10 minutes,regardless of the direction of BG change [30,31]. The formulation of thepush-pull phenomenon offered reconciliation of these results andprovided arguments for a more complex BG-IG relationship than a simpleconstant or directional time lag [34,36]. In addition, errors fromcalibration, loss of sensitivity, and random noise confound CGM data[37]. Nevertheless, the accuracy of CGM is increasing and may bereaching a physiological limit for subcutaneous glucose monitoring[38,39,40].

The Artificial Pancreas

The next step in the progression of diabetes management is automatedglucose control, or the artificial pancreas, which links a continuousglucose monitor with an insulin pump. A key element of this combinationis a closed-loop control algorithm or method, which monitors bloodglucose fluctuations and the actions of the insulin pump, and recommendsinsulin delivery at appropriate times.

The artificial pancreas idea can be traced back to developments thattook place over thirty years ago when the possibility for external BGregulation in people with diabetes had been established by studies usingintravenous (i.v.) glucose measurement and i.v. infusion of glucose andinsulin. Systems such as the Biostator™ have been introduced and used inhospital settings to maintain normoglycemia (or euglycemia) by exertingboth positive (via glucose or glucagon) and negative (via insulin)control [51,52,53,54,55]. Detailed descriptions of the major earlydesigns can be found in [56,57,58,59,60,61]. More work followed,spanning a broader range of BG control techniques, powered byphysiologic mathematical modeling and computer simulation control[62,63,64,65]. A review of methods for i.v. glucose control can be foundin [66]. However, i.v. closed-loop control remains cumbersome andunsuited for outpatient use. An alternative to extracorporeal i.v.control has been presented by implantable intra-peritoneal (i.p.)systems employing intravenous BG sampling and i.p. insulin delivery[67,68]. The implementation of these systems, however, requiresconsiderable surgery. Thus, with the advent of minimally-invasivesubcutaneous (s.c.) CGM, increasing academic and industrial effort hasbeen focused on the development of s.c.-s.c. systems, using CGM coupledwith an insulin infusion pump and a control algorithm or method[69,70,71,72]. In September 2006, the Juvenile Diabetes ResearchFoundation (JDRF) initiated the Artificial Pancreas Project and funded aconsortium of centers to carry closed-loop control research [73]. Sofar, encouraging pilot results have been reported by several centers[74,75,76,77,78].

Thus, in the past 30 years the monitoring and control of BG levels indiabetes has progressed from assessment of average glycemia once inseveral months, through daily SMBG, to minutely CGM. The increasingtemporal resolution of the monitoring technology has enabledincreasingly intensive diabetes treatment, from daily insulin injectionsor oral medication, through insulin pump therapy, to the artificialpancreas of the near future.

BRIEF SUMMARY OF THE INVENTION

As evident from the discussion above, a multitude of methods exist forBG monitoring and control in diabetes, ranging from traditional SMBG,medication, and MDI treatment, to CGM and artificial pancreas. Thesemethods are currently dissimilar and there is no system that can handlemore than one monitoring or control method at a time. An aspect of anembodiment of the present invention introduces the first flexible systemcapable of utilizing data from different monitoring techniques andcapable of providing assistance to patients with diabetes at severalscalable levels, ranging from advice about long-term trends andprognosis to real-time automated closed-loop control (artificialpancreas). These scalable monitoring and treatment strategies aredelivered by a unified system—named by the present inventors as theDiabetes Assistant (DiAs) platform—that provides a foundation forimplementation of various monitoring, advisory, and automated diabetestreatment algorithms or methods. The DiAs recommendations are tailoredto the specifics of an individual patient, and to the patient riskassessment at any given moment. Some non-limiting and exemplary uniquecharacteristics of DiAs are:

-   -   Informed by a Body Sensor Network;    -   Modular—layered architecture distributes data processing tasks        across various application modules; individual modules are        easily replaceable;    -   Scalable—naturally support new and expanded functionality,        multiple data sources, and multiple data utilization strategies;    -   Portable—DiAs can run easily on portable computing devices, such        as a cell phone, tablet computer, portal digital assistant        (PDA), etc; thus it is deployable on a wide variety of rugged,        inexpensive, and readily available devices;    -   Local and Global modes of operation—certain processes and        patient interactions are available through the portable device;        other services and remote monitoring of subject and system        states are available via wireless communications (e.g. 3G, WiFi,        etc.).

According to one aspect of the invention, a system is provided formanaging glycemic control of a patient, comprising an input moduleconfigured to accept input data from one or more of a plurality ofdiverse blood glucose measurement devices and one or more of a pluralityof diverse insulin delivery devices; a data classifier module configuredto classify data accepted by said input module and to determineappropriate processing of said input data according to itsclassification; a patient state estimation module configured to processinput data in accordance with at least one data processing algorithmcorresponding to the classification of the input data as determined bythe data classifier module; a patient risk status module configured todetermine a level of risk of said patient with respect to abnormalglycemic states using processed data from said patient state estimationmodule; and an output module configured to output advisory messages,patient alerts, and control signals for said blood glucose measurementdevices and said insulin delivery devices based on the level of riskdetermined by said patient risk status module.

According to another aspect of the invention, a non-transitorycomputer-readable storage medium is provided containingcomputer-executable instructions for performing functions to carry outthe system.

BRIEF SUMMARY OF THE DRAWINGS

FIG. 1 is a schematic illustration of the DiAs platform inputs andoutputs according to an aspect of the invention;

FIG. 2 is a schematic illustration of DiAs processes and servicesaccording to an aspect of the invention;

FIG. 3 is a block diagram of the DiAs system including applications andcommunication functions according to an embodiment of the invention;

FIG. 4 shows an example implementation of the DiAs system on a cellphone platform according to an embodiment of the invention;

FIG. 5 is a schematic illustration of an implementation of the DiAssystem as a hub for a body sensor network; and

FIG. 6 is a schematic block diagram of an example data processing systemfor implementation of the present invention in whole or in part.

DETAILED DESCRIPTION OF THE INVENTION

Overview

As shown in FIG. 5, a principal application of the DiAs system is thedynamic aggregation of body sensor network (BSN) data toward the goal ofsupporting long-term and efficient treatment of diabetes. DiAs is basedon a wearable or handheld Diabetes Assistant platform that collects andpre-processes data from each individual's BSN, and uploads summarystatistics to a remote location. The interface/algorithmic/methodologyframework of DiAs: (i) ensures plug and play functionality withdifferent metabolic sensors, (ii) allows for a general framework forprioritization of sensor data, making it clear how scarce computational,memory, and communication resources will be allocated to various sensingmodalities, (iii) manages access to multiple uplink channels of varyingreliability to a remote site, and (iv) resolves tradeoffs relating towhere heavy computations should be performed (e.g., locally within theDiAs platform or remotely).

DiAs Inputs and Outputs

FIG. 1 presents the data sources available to the DiAs platform and theoutput services that DiAs provides. The data sources include SMBG, CGM,insulin delivery data (MDI and CSII), and other BSN data inputs such asheart rate, body accelerometer data, blood pressure, respiration, EKGdata, etc. Depending on data availability (intermittent or continuous,blood glucose alone, or a multivariate data stream), DiAs providesdifferent types of services that can be generally classified as:

-   -   Local Services: Applications that run on a portable device (e.g.        a cell phone or tablet computer) communicating with an array of        self-SMBG monitoring and CGM devices, an array of insulin        delivery devices, and with other sensors in a BSN. The local        service of DiAs is equipped with intelligent processing to        provide an array of patient services, including safety        supervision, local alerts, patient advisory functions, and        closed-loop control (described further below);    -   Global Services: A centralized server communicating with        multiple local services to provide different levels of data        processing, advice, and training to patients; enable remote        monitoring of glucose control profiles (e.g. parents monitoring        remotely their children with diabetes); enable global alerts        (e.g. a 911 call with GPS service to pinpoint a patient in need        of emergency assistance), and to provide a physician-oriented        information service presenting key data for multiple patients at        a glance.        DiAs Processes

The general flow of DiAs processes is presented in FIG. 2 and includesthe following steps:

-   -   1. Incoming data are directed to a Data Availability Classifier        (DAC), which assesses the frequency, dimensionality, and quality        of the incoming data. Based on the assessment, the DAC        recommends different classes of data processing algorithms for        the incoming data. Many of these algorithms already exist and        are generally known, and can be classified as follows:        -   SMBG: This is currently the most established algorithmic            class, including methods for the retrieval of SMBG data,            evaluation of glycemic control, estimation of the risk for            hypoglycemia, and information displays. SMBG acquisition and            processing methods are described in several U.S. patents and            published patent applications (see references [79-85]            incorporated herein by reference). A 5-year clinical trial            testing a SMBG-based system in 120 people with T1DM was            recently completed, resulting in improved glycemic control,            reduction of the risk for severe hypoglycemia, and high            patient approval rating (results published in [86]);        -   CGM: Key elements applicable to these methods have been            defined (references [87-92]). These methods are currently            under development and testing in a large NIH-funded research            project (Grant RO1 DK 085623, Principal Investigator Dr.            Boris Kovatchev);        -   CGM+insulin pump: Most of the methods applicable to CGM            alone have extensions capable of dealing with input/output            to/from an insulin pump. We have recently completed an            extensive series of clinical trials of closed-loop control            to date.        -   Other: Heart rate changes can be used to indicate periods of            physical activity, and more specifically periods of            increased insulin sensitivity associated with exercise.            These data inform diabetes control at several levels,            including risk assessment for hypoglycemia and closed-loop            control [93,94].    -   2. The first step of data processing is Patient State        Estimation, given available data and using one of the methods        described above. The state estimation results in assessment of        the patient's risk status, which can be based on the risk        analysis metrics presented in the background discussion above,        and on biosystem observers or sensors, which process physiologic        (and possibly behavioral) data to produce quantitative biosystem        state estimators. These algorithms or methods are based on        underlying mathematical models of the human metabolism and a        Kalman filter, which produces system state estimation. Each        system state estimator is a physiological or behavioral        parameter of importance to the functioning of a person. The        ensemble (vector) of biosystem estimators for a particular        person represents the status of this person in terms of the        blood glucose trend, availability of insulin, and risk for        hypoglycemia. In essence, biosystem observers personalize the        metabolic observation to a specific subject and extract        composite information from the vast array of raw data that        allows the precise evaluation of the subject's condition. It is        anticipated that the biosystem observers will reside within a        wearable DiAs system, while their summarized output will be sent        to both the local predictive and control algorithms or methods        and to remote observers as follows:    -   The primary output from the Patient State Estimation will be        assessment of the patient's risk status for hypo- or        hyperglycemia, based on the risk analysis and the LBGI/HBGI        presented above. If the data quality and density is adequate for        the risk status of the patient (e.g. the patient is in a steady        state performing regular SMBG resulting in LBGI and HBGI lower        than certain preset thresholds), then DiAs refers the data to        algorithms that maintain the current patient status or fine-tune        the patient's glycemic control. These algorithms can work in        either an advisory or automated (closed-loop control) mode as        follows:        -   In advisory mode, DiAs activates the following services            modules:            -   Advisory Module 1: Prediction of elevated risk for                hypoglycemia (24 hours ahead);            -   Advisory Module 2: Bolus calculator suggesting pre-meal                insulin doses;            -   Advisory Module 3: Suggestion of basal rate profiles for                the next 24 hours.        -   In closed-loop control mode, DiAs activates the following            service modules:            -   Control Module 1: Real-time detection and prevention of                hypoglycemia;            -   Control Module 2: Stochastic control of pre-meal insulin                boluses, and            -   Control Module 3: Deterministic control of basal rate                and overnight steady state.    -   If the data quality and density is inadequate for the risk        status of the patient (e.g. the patient is at high risk for        hypoglycemia, hyperglycemia, or both as indicated by the LBGI        and HBGI exceeding certain preset thresholds), then:        -   In advisory mode, DiAs recommends enhanced monitoring (e.g.            more frequent SMBG or switching to CGM for a certain period            of time);        -   In automated control mode, DiAs switches the monitoring            device to higher frequency SMBG measurement or to CGM mode            (Note: such flexible monitoring devices are not currently            manufactured, but are anticipated to be available in the            future).

FIG. 3 presents a detailed schematic of the DiAs architecture:

-   -   Central to this architecture is the Biometric State Estimator,        which is the hub for exchange of data between the DiAs        monitoring devices and algorithmic services or related methods.        The Biometric State Estimator may also exchange data with remote        physicians and/or patient care centers over the Internet through        a network interface;    -   The inputs used for state estimation are provided by various        peripheral devices that monitor blood glucose fluctuations (SMBG        Service, CGM Service), execute insulin delivery (Pump Service),        or monitor other physiological parameters (Heart Rate Service,        Esc. Service) as shown in FIG. 3;    -   In turn, the Biometric State Estimator provides feedback to        these devices as determined by a Safety Service, which assesses        the integrity of the received data and judges whether the        peripheral input/output devices are functioning properly.        Methods employed by the Safety Service include previously        introduced detection of CGM sensor errors [91] or judging the        safety of insulin delivery [92];    -   DiAs Applications may include various advisory and/or control        algorithms, system and patient state alarms and indicators.        These applications may be external to the DiAs system, and may        be developed by third parties. Such applications may use DiAs        services provided that they comply with the data exchange        standards of the system. For example, a Hyperglycemia Mitigation        Service (HMS) is a closed-loop control algorithm or method        included in one of the embodiments of DiAs;

The user interface with the DiAs system can be custom designed to meetthe needs of specific DiAs implementations. One such implementation of auser interface is shown in FIG. 4:

-   -   Two “traffic lights” signify the patient's present risk status        for hypoglycemia and hyperglycemia, respectively, indicating low        risk (green light), moderate risk/system action to mitigate the        risk (yellow light) and high risk/necessity for immediate human        intervention (red light);    -   Several system/patient status inquiry icons open additional        interfaces allowing the patient to access graphical and        numerical representation of his/her glucose control, or inform        the system of events (such as carbohydrate intake or exercise),        which are treated as additional inputs by the DiAs analytical        system;    -   Network service (described in the next section) ensures remote        monitoring and transmission of alerts and critical information        in high-risk states.        Implementation of DiAs

FIG. 5 shows two major components of a DiAs implementation as a BodySensor Network:

-   -   Local Services (within the wearable/portable DiAs device) use        predictive and control algorithms or methods based on simplified        models of the human metabolic system that are trackable in real        time. These are simple, typically linearized macro-level models        that focus only on the principal system components. One example        of such a model is the classic Minimal Model of Glucose Kinetics        developed 30 years ago [95]. Available algorithms or methods        include assessment, prediction, and control of glucose        fluctuations in diabetes:        -   Risk analysis of metabolic state with respect to normative            limits;        -   Detection of abrupt system changes, i.e. transitions of the            system (person) from a stable to a critical state;        -   Prediction of trends and gradual system changes, and outcome            evaluation;        -   Estimation of the probability for abrupt critical            transitions;        -   Warnings, alarms, and advisory messages when critical            thresholds are approached;        -   Automated intervention to prevent critical events;        -   Communication to remote location and global algorithms or            methods.    -   As shown in FIG. 5, a portable DiAs device (such as shown in        FIG. 4) is communicatively connected (e.g. wirelessly through a        wireless communication protocol such as Bluetooth, IEEE 802.11,        etc.) to a plurality of BSN sensors, such as an ICP sensor, ECG        sensor, blood pressure sensor, pulse oximetry sensor, inertial        sensor, EMG sensor, artificial pancreas sensor, etc.        Additionally, the DiAs device may have an interface to accept        SMBG data.    -   Global services rely on predictive and control algorithms or        methods deployed at a central location and receiving information        from an array of individual system observers. These algorithms        or methods will be based on large-scale probability models, risk        analysis, clustering, and discriminant algorithms or methods.        The output of these algorithms or methods will allow:        -   The monitoring of vital signs and metabolic processes by            health care providers;        -   The detection of critical cases that require immediate            intervention;        -   Collection of population-level anonymous public health            statistics of interest to health care organizations.    -   Software/Hardware Implementation: Central to DiAs is a scalable        software stack with a modular design that can be efficiently        adapted to a variety of hardware platforms. The software        architecture, the availability of suitable hardware platforms        and opportunities to transfer software modules to commercial        partners will factor into the choice of DiAs operating systems.        For clinical trials and ambulatory implementation, hardware is        needed that is portable, rugged, reliable, inexpensive and        easily available. In this regard, a cell phone or a tablet        computer could be selected. Consequently, the DiAs system may        run within a customized version of the Android operating system.        Android has a robust development environment, is available with        source code, is backed by Google and runs on an ever-increasing        array of cell phones and tablets from a variety of        manufacturers. Android is being adopted by many commercial        developers for new embedded software projects. Although many        current products with embedded control software either have no        operating system at all or use a simple control loop the trend        is towards basing new embedded software projects on Android and        embedded Linux. Since Android is built on top of Linux, an        Android-based operating system for DiAs would allow transfer of        software code to industry partners for commercial use. Android        also provides a rich software development kit that supports        multi-touch graphical user interface design, data        communications, geo-location and telephony. Specifically:        -   At the highest level the AAPP Software Stack is composed of            three major functional blocks: Device I/O Services, Core            Services, and Control. As described above, FIG. 3 presents a            diagram of the software stack depicting these blocks.        -   Device I/O Services handles all communication with sensors,            pumps and other devices and provides a data interface to            other elements of the system. The Device I/O modules store            SMBG, CGM, and delivered insulin data and provide it to            other components upon request.        -   Device I/O modules also implement a sensor and pump command            service that validates and delivers commands received from            the Safety Service.        -   Core Services is responsible for providing a runtime            environment for applications such as the Closed-Loop Control            App or the User Advice App and for supervising their            operation. It generates state estimates based upon available            data and provides this data to applications upon request.        -   Safety Service screens insulin bolus commands for safety            before delivering them to the pump module and monitors the            functioning of I/O devices detecting errors and potentially            unsafe deviations.    -   While a preferred operating system has been discussed above, it        will be recognized by those skilled in the art that the DiAs        system may be implemented using any operating system that has        features necessary to implement the DiAs system as contemplated        above.

Turning now to FIG. 6, a functional block diagram is shown for acomputer system 600 for exemplary implementation of an embodiment orportion of an embodiment of the present invention. For example, a methodor system of an embodiment of the present invention may be implementedusing hardware, software or a combination thereof and may be implementedin one or more computer systems or other processing systems, such aspersonal digit assistants (PDAs) equipped with adequate memory andprocessing capabilities. In an example embodiment, the invention wasimplemented in software running on a general purpose computer 600 asillustrated in FIG. 6. The computer system 600 may includes one or moreprocessors, such as processor 604. The Processor 604 is connected to acommunication infrastructure 606 (e.g., a communications bus, cross-overbar, or network). The computer system 600 may include a displayinterface 602 that forwards graphics, text, and/or other data from thecommunication infrastructure 606 (or from a frame buffer not shown) fordisplay on the display unit 630. Display unit 630 may be digital and/oranalog.

The computer system 600 may also include a main memory 608, preferablyrandom access memory (RAM), and may also include a secondary memory 610.The secondary memory 610 may include, for example, a hard disk drive 612and/or a removable storage drive 614, representing a floppy disk drive,a magnetic tape drive, an optical disk drive, a flash memory, etc. Theremovable storage drive 614 reads from and/or writes to a removablestorage unit 618 in a well known manner. Removable storage unit 618,represents a floppy disk, magnetic tape, optical disk, etc. which isread by and written to by removable storage drive 614. As will beappreciated, the removable storage unit 618 includes a computer usablestorage medium having stored therein computer software and/or data.

In alternative embodiments, secondary memory 610 may include other meansfor allowing computer programs or other instructions to be loaded intocomputer system 600. Such means may include, for example, a removablestorage unit 622 and an interface 620. Examples of such removablestorage units/interfaces include a program cartridge and cartridgeinterface (such as that found in video game devices), a removable memorychip (such as a ROM, PROM, EPROM or EEPROM) and associated socket, andother removable storage units 622 and interfaces 620 which allowsoftware and data to be transferred from the removable storage unit 622to computer system 600.

The computer system 600 may also include a communications interface 624.Communications interface 124 allows software and data to be transferredbetween computer system 600 and external devices. Examples ofcommunications interface 624 may include a modem, a network interface(such as an Ethernet card), a communications port (e.g., serial orparallel, etc.), a PCMCIA slot and card, a modem, etc. Software and datatransferred via communications interface 624 are in the form of signals628 which may be electronic, electromagnetic, optical or other signalscapable of being received by communications interface 624. Signals 628are provided to communications interface 624 via a communications path(i.e., channel) 626. Channel 626 (or any other communication means orchannel disclosed herein) carries signals 628 and may be implementedusing wire or cable, fiber optics, blue tooth, a phone line, a cellularphone link, an RF link, an infrared link, wireless link or connectionand other communications channels.

In this document, the terms “computer program medium” and “computerusable medium” are used to generally refer to media or medium such asvarious software, firmware, disks, drives, removable storage drive 614,a hard disk installed in hard disk drive 612, and signals 628. Thesecomputer program products (“computer program medium” and “computerusable medium”) are means for providing software to computer system 600.The computer program product may comprise a computer useable mediumhaving computer program logic thereon. The invention includes suchcomputer program products. The “computer program product” and “computeruseable medium” may be any computer readable medium having computerlogic thereon.

Computer programs (also called computer control logic or computerprogram logic) are may be stored in main memory 608 and/or secondarymemory 610. Computer programs may also be received via communicationsinterface 624. Such computer programs, when executed, enable computersystem 600 to perform the features of the present invention as discussedherein. In particular, the computer programs, when executed, enableprocessor 604 to perform the functions of the present invention.Accordingly, such computer programs represent controllers of computersystem 600.

In an embodiment where the invention is implemented using software, thesoftware may be stored in a computer program product and loaded intocomputer system 600 using removable storage drive 614, hard drive 612 orcommunications interface 624. The control logic (software or computerprogram logic), when executed by the processor 604, causes the processor604 to perform the functions of the invention as described herein.

In another embodiment, the invention is implemented primarily inhardware using, for example, hardware components such as applicationspecific integrated circuits (ASICs). Implementation of the hardwarestate machine to perform the functions described herein will be apparentto persons skilled in the relevant art(s).

In yet another embodiment, the invention is implemented using acombination of both hardware and software.

In an example software embodiment of the invention, the methodsdescribed above may be implemented in SPSS control language or C++programming language, but could be implemented in other variousprograms, computer simulation and computer-aided design, computersimulation environment, MATLAB, or any other software platform orprogram, windows interface or operating system (or other operatingsystem) or other programs known or available to those skilled in theart.

REFERENCES

The following patents, applications and publications as listed below andthroughout this document are hereby incorporated by reference in theirentirety herein.

-   1. Reichard P, Phil M. Mortality and treatment side effects during    long-term intensified conventional insulin treatment in the    Stockholm Diabetes Intervention study. Diabetes 43: 313-317, 1994-   2. The Diabetes Control and Complications Trial Research Group. The    effect of intensive treatment of diabetes on the development and    progression of long-term complications of insulin-dependent diabetes    mellitus. N Engl J Med 329: 978-986, 1993-   3. UK Prospective Diabetes Study Group (UKPDS). Intensive    blood-glucose control with sulphonylureas or insulin compared with    conventional treatment and risk of complications in patients with    type 2 diabetes. Lancet 352: 837-853, 1998-   4. Cryer P E. Hypoglycaemia: The limiting factor in the glycaemic    management of type I and type II diabetes. Diabetologia 45: 937-948,    2002-   5. Cryer P E: Hypoglycemia: The Limiting factor in the management of    IDDM. Diabetes 43: 1378-1389, 1994-   6. Clarke W L, Cox D, Gonder-Frederick L A, Carter W, Pohl S L.    Evaluating the clinical accuracy of self-blood glucose monitoring    systems. Diabetes Care, 10: 622-628, 1987.-   7. The diabetes research in children network (DirecNet) study group.    A multicenter study of the accuracy of the One Touch® Ultra® home    glucose meter in children with Type 1 diabetes. Diabetes Technol    Ther, 5: 933-942, 2003-   8. Kovatchev B P, Cox D J, Gonder-Frederick L A Young-Hyman D,    Schlundt D, Clarke W L. Assessment of risk for severe hypoglycemia    among adults with IDDM: Validation of the Low Blood Glucose Index,    Diabetes Care 21: 1870-1875, 1998.-   9. Cox D J, Kovatchev B, Julian D, Gonder-Frederick L A, Polonsky W    H, Schlundt D G, Clarke W L. Frequency of severe hypoglycemia in    IDDM can be predicted from self-monitoring blood glucose data. J    Clin Endocrinol Metab 79: 1659-1662, 1994.-   10. Kovatchev B P, Cox D J, Kumar A, Gonder-Frederick L A and W L    Clarke. Algorithmic Evaluation of Metabolic Control and Risk of    Severe Hypoglycemia in Type 1 and Type 2 Diabetes Using    Self-Monitoring Blood Glucose (SMBG) Data. Diabetes Technol Ther, 5    (5): 817-828, 2003.-   11. Cox D J, Gonder-Frederick L A, Ritterband L, Clarke W L, and    Kovatchev B P. Prediction of Severe Hypoglycemia. Diabetes Care, 30:    1370-1373, 2007.-   12. Kovatchev B P, Cox D J, Farhy L S, Straume M, Gonder-Frederick L    A, Clarke, W L. Episodes of Severe Hypoglycemia in Type 1 Diabetes    are Preceded, and Followed, within 48 Hours by Measurable    Disturbances in Blood Glucose. J Clin Endocrinol Metab, 85:    4287-4292, 2000.-   13. Kovatchev B P, Straume M, Cox D J, Farhy L S. Risk analysis of    blood glucose data: A quantitative approach to optimizing the    control of insulin dependent diabetes. J of Theoretical Medicine,    3:1-10, 2001.-   14. Kovatchev B P, Cox D J, Gonder-Frederick L A and W L Clarke.    Symmetrization of the blood glucose measurement scale and its    applications. Diabetes Care, 20: 1655-1658, 1997.-   15. Mastrototaro J. J. The MiniMed Continuous Glucose Monitoring    System. Diabetes Technol Ther, 2: Supplement 1: S-13-S-18, 2000.-   16. Bode B. W. Clinical Utility of the Continuous Glucose Monitoring    System. Diabetes Technol Ther, 2: Supplement 1: S-35-S-42, 2000.-   17. Feldman B, Brazg R, Schwartz S, Weinstein R. A continuous    glucose sensor based on wired enzyme technology—results from a 3-day    trial in patients with Type 1 diabetes. Diabetes Technol Ther, 5:    769-778, 2003.-   18. The diabetes research in children network (DirecNet) study    group. The accuracy of the CGMS™ in children with Type 1 diabetes:    Results of the diabetes research in children network (DirecNet)    accuracy study. Diabetes Technol Ther, 5: 781-790, 2003.-   19. Kovatchev B P, Anderson S M, Heinemann L, Clarke W L. Comparison    of the numerical and clinical accuracy of four continuous glucose    monitors. Diabetes Care, 31: 1160-1164, 2008.-   20. Deiss, D, Bolinder, J, Riveline, J, Battelino, T, Bosi, E,    Tubiana-Rufi, N, Kerr, D, Phillip, M: Improved glycemic control in    poorly controlled patients with type 1 diabetes using real-time    continuous glucose monitoring. Diabetes Care 29: 2730-2732, 2006.-   21. Garg, K, Zisser, H, Schwartz, S, Bailey, T, Kaplan, R, Ellis, S,    Jovanovic, L: Improvement in Glycemic Excursions With a    Transcutaneous, Real-time Continuous Glucose Sensor. Diabetes Care    29:44-50, 2006.-   22. Kovatchev B P, Clarke W L. Continuous glucose monitoring reduces    risks for hypo- and hyperglycemia and glucose variability in    diabetes. Diabetes, 56, Supplement 1: 0086OR, 2007.-   23. The Juvenile Diabetes Research Foundation Continuous Glucose    Monitoring Study Group: Continuous glucose monitoring and intensive    treatment of type 1 diabetes. N Engl J Med, 359:1464-76, 2008.-   24. Klonoff D C: Continuous glucose monitoring: roadmap for 21^(st)    century diabetes therapy. Diabetes Care 28:1231-1239, 2005.-   25. Hovorka R: Continuous glucose monitoring and closed-loop    systems. Diabet Med 23:1-12, 2006.-   26. Klonoff D C: The Artificial Pancreas: How Sweet Engineering Will    Solve Bitter Problems. J Diabetes Sci Technol, 1: 72-81, 2007.-   27. Hirsch I B, Armstrong D, Bergenstal R M, Buckingham B, Childs B    P, Clarke W L, Peters A, Wolpert H. Clinical Application of Emerging    Sensor Technologies in Diabetes Management: Consensus Guidelines for    Continuous Glucose Monitoring. Diabetes Tech Ther, 10: 232-246,    2008.-   28. Rebrin K, Steil G M, van Antwerp W P, and Mastrototaro J J:    Subcutaneous glucose predicts plasma glucose independent of insulin:    implications for continuous monitoring. Am J Physiol Endocrinol    Metab, 277:E561-E571, 1999.-   29. Rebrin K and Steil G M: Can interstitial glucose assessment    replace blood glucose measurements? Diabetes Technol Ther,    2:461-472, 2000.-   30. Steil G M, Rebrin K, Hariri F, Jinagonda S, Tadros S, Darwin C,    Saad M F: Interstitial fluid glucose dynamics during insulin-induced    hypoglycaemia. Diabetologia, 48:1833-40, 2005.-   31. Boyne M, Silver D, Kaplan J, and Saudek C: Timing of Changes in    Interstitial and Venous Blood Glucose Measured With a Continuous    Subcutaneous Glucose Sensor. Diabetes, 52:2790-2794, 2003.-   32. Kulcu E, Tamada J A, Reach G, Potts R O, Lesho M J:    Physiological differences between interstitial glucose and blood    glucose measured in human subjects. Diabetes Care, 26:2405-2409,    2003.-   33. Stout P J, Racchini J R, Hilgers M E: A Novel Approach to    Mitigating the Physiological Lag between Blood and Interstitial    Fluid Glucose Measurements. Diabetes Technol Ther, 6:635-644, 2004.-   34. Wentholt I M E, Hart A A M, Hoekstra J B L, DeVries J H.    Relationship Between Interstitial and Blood Glucose in Type 1    Diabetes Patients: Delay and the Push-Pull Phenomenon Revisited.    Diabetes Technol Ther, 9:169-175, 2004.-   35. Wientjes K J, Schoonen A J: Determination of time delay between    blood and interstitial adipose tissue glucose concentration change    by microdialysis in healthy volunteers. Int J Artif Organs,    24:884-889, 2001.-   36. Aussedat B, Dupire-Angel M, Gifford R, Klein J C, Wilson G S,    Reach G: Interstitial glucose concentration and glycemia:    implications for continuous subcutaneous glucose monitoring. Am J    Physiol Endocrinol Metab, 278:E716-E728, 2000.-   37. Kovatchev B P and Clarke W L. Peculiarities of the Continuous    Glucose Monitoring Data Stream and Their Impact on Developing    Closed-Loop Control Technology. J Diabetes Sci Technol, 2:158-163,    2008.-   38. Clarke W L, Kovatchev B P: Continuous glucose sensors—continuing    questions about clinical accuracy. J Diabetes Sci Technol 1:164-170,    2007.-   39. The Diabetes Research in Children Network (DirecNet) Study    Group. The Accuracy of the Guardian® RT Continuous Glucose Monitor    in Children with Type 1 Diabetes. Diabetes Tech Ther, 10: 266-272,    2008.-   40. Garg S K, Smith J, Beatson C, Lopez-Baca B, Voelmle M, Gottlieb    P A. Comparison of Accuracy and Safety of the SEVEN and the    Navigator Continuous Glucose Monitoring Systems. Diabetes Tech Ther,    11: 65-72, 2009.-   41. T. Heise, T. Koschinsky, L. Heinemann, and V. Lodwig, “Glucose    Monitoring Study Group. Hypoglycemia warning signal and glucose    sensors: requirements and concepts,” Diabetes Technol Ther, 5:    563-571, 2003.-   42. B. Bode, K. Gross, N. Rikalo, S. Schwartz, T. Wahl, C. Page, T.    Gross, and J. Mastrototaro. Alarms based on real-time sensor glucose    values alert patients to hypo- and hyperglycemia: the guardian    continuous monitoring system. Diabetes Technol Ther, 6: 105-113,    2004.-   43. G. McGarraugh and R. Bergenstal, “Detection of hypoglycemia with    continuous interstitial and traditional blood glucose monitoring    using the FreeStyle Navigator Continuous Glucose Monitoring System,”    Diabetes Technol Ther, 11: 145-150, 2009.-   44. S. E. Noujaim, D. Horwitz, M. Sharma, J. Marhoul, “Accuracy    requirements for a hypoglycemia detector: an analytical model to    evaluate the effects of bias, precision, and rate of glucose    change,” J Diabetes Sci Technol, 1: 653-668, 2007.-   45. W. K. Ward, “The role of new technology in the early detection    of hypoglycemia,” Diabetes Technol Ther, 6: 115-117, 2004.-   46. B. Buckingham, E. Cobry, P. Clinton, V. Gage, K. Caswell, E.    Kunselman, F. Cameron, and H. P. Chase. Preventing hypoglycemia    using predictive alarm algorithms and insulin pump suspension.    Diabetes Technol Ther, 11: 93-97, 2009.-   47. Miller M, Strange P: Use of Fourier Models for Analysis and    Interpretation of Continuous Glucose Monitoring Glucose Profiles. J    Diabetes Sci Technol, 1: 630-638, 2007.-   48. Kovatchev B P, Gonder-Frederick L A, Cox D J, Clarke W L:    Evaluating the accuracy of continuous glucose-monitoring sensors:    continuous glucose-error grid analysis illustrated by TheraSense    Freestyle Navigator data. Diabetes Care, 27:1922-28, 2004.-   49. McDonnell C M, Donath S M, Vidmar S I, Werther G A, Cameron F J:    A Novel Approach to Continuous Glucose Analysis Utilizing Glycemic    Variation. Diabetes Technol Ther, 7: 253-263, 2005.-   50. Clarke W L & Kovatchev B P. Statistical Tools to Analyze CGM    Data. Diabetes Technol Ther, 11: S45-S54, 2009.-   51. Albisser A M, Leibel B S, Ewart T G, Davidovac Z, Botz C K,    Zinggg W. An artificial endocrine pancreas. Diabetes, 23:389-396,    1974.-   52. Clemens A H, Chang P H, Myers R W. The development of Biostator,    a glucose-controlled insulin infusion system. Horm Metab Res    Supplement, 7: 23-33, 1977.-   53. Marliss E B, Murray F T, Stokes E F, Zinman B, Nakhooda A F,    Denoga A, Leibel B S, and Albisser A M: Normalization of glycemia in    diabetics during meals with insulin and glucagon delivery by the    artificial pancreas. Diabetes 26: 663-672, 1977.-   54. Pfeiffer E F, Thum Ch, and Clemens A H: The artificial beta    cell—A continuous control of blood sugar by external regulation of    insulin infusion (glucose controlled insulin infusion system). Horm    Metab Res 487: 339-342, 1974.-   55. Santiago J V, Clemens A H, Clarke W L, Kipnis D M. Closed-loop    and open-loop devices for blood glucose control in normal and    diabetic subjects. Diabetes, 28: 71-84, 1979.-   56. Broekhuyse H M, Nelson J D, Zinman B, and Albisser A M:    Comparison of algorithms for the closed-loop control of blood    glucose using the artificial beta cell. IEEE Trans Biomed Eng 28:    678-687, 1981.-   57. Clemens A H: Feedback control dynamics for glucose controlled    insulin infusion system. Med Prog Technol 6: 91-98, 1979.-   58. Cobelli C, Ruggeri A: Evaluation of portal/peripheral route and    of algorithms for insulin delivery in the closed-loop control of    glucose in diabetes. A modeling study. IEEE Trans Biomed Eng 30:    93-103, 1983.-   59. E W, Campbell L V, Chia Y O, Meler H, and Lazarus L: Control of    blood glucose in diabetics using an artificial pancreas. Aust N Z J    Med 7: 280-286, 1977.-   60. Fischer U, Jutzi E, Freyse E-J, and Salzsieder E: Derivation and    experimental proof of a new algorithm for the artificial beta-cell    based on the individual analysis of the physiological    insulin-glucose relationship. Endokrinologie 71:65-75, 1978.-   61. Salzsieder E, Albrecht G, Fischer U, and Freyse E-J: Kinetic    modeling of the gluco-regulatory system to improve insulin therapy.    IEEE Trans Biomed Eng 32: 846-855, 1985.-   62. Brunetti P., Cobelli C., Cruciani P., Fabietti P. G., Filippucci    F., Santeusanio F.: A simulation study on a self-tuning portable    controller of blood glucose. Int J Artificial Organs 16: 51-57,    1993.-   63. Fischer U, Schenk W, Salzsieder E, Albrecht G, Abel P, and    Freyse E-J: Does physiological blood glucose control require an    adaptive strategy? IEEE Trans Biomed Eng 34:575-582, 1987.-   64. Sorensen J T: A Physiologic Model of Glucose Metabolism in Man    and its Use to Design and Assess Improved Insulin Therapies for    Diabetes, Ph.D. dissertation, Dept Chemical Engineering, MIT, 1985.-   65. Parker R S, Doyle F J 3rd, Peppas N A. A model-based algorithm    for blood glucose control in Type I diabetic patients. IEEE Trans    Biomed Eng, 48:148-157, 1999.-   66. Parker R S, Doyle F J 3rd, Peppas N A. The intravenous route to    blood glucose control. IEEE Eng Med Biol, 20:65-73, 2001.-   67. Leblanc H, Chauvet D, Lombrail P, Robert J J: Glycemic control    with closed-loop intraperitoneal insulin in type I diabetes.    Diabetes Care, 9: 124-128, 1986.-   68. Renard E: Implantable closed-loop glucose-sensing and insulin    delivery: the future for insulin pump therapy, Current Opinion in    Pharmacology, 2: 708-716, 2002.-   69. Bellazzi R, Nucci G, Cobelli C: The subcutaneous route to    insulin-dependent diabetes therapy: closed-loop and partially    closed-loop control strategies for insulin delivery and measuring    glucose concentration. IEEE Eng Med Biol, 20: 54-64, 2001.-   70. Hovorka R, Chassin L J, Wilinska M E, et al. Closing the loop:    the ADICOL experience. Diabetes Technol Ther. 6: 307-318, 2004.-   71. Steil G M, Rebrin K, Darwin C, Hariri F, Saad M F. Feasibility    of automating insulin delivery for the treatment of type 1 diabetes.    Diabetes, 55: 3344-3350, 2006.-   72. Clarke W L and Kovatchev B P. The Artificial Pancreas: How Close    We Are to Closing the Loop? Ped Endocrinol Rev, 4: 314-316, 2007.-   73. The JDRF e-Newsletter: Emerging Technologies in Diabetes    Research, September, 2006.-   74. Weinzimer S A, Steil G M, Swan K L, Dziura J, Kurtz N,    Tamborlane W V: Fully automated closed-loop insulin delivery versus    semi-automated hybrid control in pediatric patients with type 1    diabetes using an artificial pancreas. Diabetes Care, 31:934-939,    2008.-   75. Clarke W L, Anderson S M, Breton M D, Patek S D, Kashmer L, and    Kovatchev B P. Closed-Loop Artificial Pancreas Using Subcutaneous    Glucose Sensing and Insulin Delivery and a Model Predictive Control    Algorithm: The Virginia Experience. J Diabetes Sci Technol, 3:    1031-1038, 2009.-   76. Bruttomesso D, Farret A, Costa S, Marescotti M C, Vettore M,    Avogaro A, Tiengo A, C. et al: Closed-Loop Artificial Pancreas Using    Subcutaneous Glucose Sensing & Insulin Delivery, and a Model    Predictive Control Algorithm: Preliminary Studies in Padova and    Montpellier. J Diabetes Sci Technol, 3: 1014-1021, 2009.-   77. Hovorka R, Allen J M, Elleri D, et al., Manual closed-loop    insulin delivery in children and adolescents with type 1 diabetes: a    phase 2 randomised crossover trial. The Lancet, 375: 743-751, 2010.-   78. El-Khatib F H, Russell S J, Nathan D M, Sutherlin R G, Damiano    E R. A Bihormonal Closed-Loop Artificial Pancreas for Type 1    Diabetes. Science Transl Med, 2: 27ra27, 2010.-   79. Kovatchev B P & Cox D J. Method, system, and computer program    product for the evaluation of glycemic control in diabetes from    self-monitoring data; U.S. Pat. No. 7,025,425 issued on Apr. 11,    2006.-   80. Kovatchev B P & Cox D J. Method, system, and computer program    product for the evaluation of glycemic control in diabetes from    self-monitoring data; U.S. Pat. No. 7,874,985 B2 issued on Jan. 25,    2011.-   81. Kovatchev B P. Method, system, and computer program product for    evaluation of blood glucose variability in diabetes from    self-monitoring data; PCT/US2007/000370; 2007.-   82. Kovatchev B P. Systems, methods and computer program codes for    recognition of patterns of hyperglycemia and hypoglycemia, increased    glucose variability, and ineffective self-monitoring in diabetes.    PCT/US2008/0154513 A1, 2008.-   83. Kovatchev B P and Breton M D. Method, System, and Computer    Program Product for Visual and Quantitative Tracking of Blood    Glucose Variability in Diabetes from Self-Monitoring Data. U.S.    Provisional PCT/US2009/065725, 2009.-   84. International Patent Application Serial No. PCT/US2010/047711,    Kovatchev, et al., “Tracking the Probability for Imminent    Hypoglycemia in Diabetes from Self-Monitoring Blood Glucose (SMBG)    Data”, filed Sep. 2, 2010.-   85. Kovatchev B P and Breton M D. Method, system and computer    program product for evaluation of insulin sensitivity,    insulin/carbohydrate ratio, and insulin correction factors in    diabetes from self-monitoring data; PCT/US2008/069416 and U.S.    Publication Application US2010/0198520.-   86. Kovatchev B P, Mendosa P, Anderson S M, Hawley J S, Ritterband L    M, & Gonder-Frederick L. Effect of automated bio-behavioral feedback    on the control of type 1 diabetes. Diabetes Care, 34:302-307, 2011-   87. Kovatchev B P, Gonder-Frederick L A, Cox D J, Clarke W L.    Method, system and computer program for evaluating the accuracy of    blood glucose monitoring sensors/devices, U.S. Pat. No. 7,815,569    issued on Oct. 19, 2010.-   88. Breton M D and Kovatchev B P. Method, system and computer    program product for real-time detection of sensitivity decline in    analyte sensors; PCT/US2007/082744, 2007.-   89. Patek S D and Breton M D. LQG Artificial Pancreas Control System    And Related Method. International Application Serial No.    PCT/US2008/067723.-   90. Kovatchev B P, Breton M D, and Patek S D. Method, System and    Computer Program Product for CGM-Based Prevention of Hypoglycemia    Risk Assessment and Smooth Reduction of Insulin. International    Application Serial No. PCT/US2010/025405.-   91. A. International Patent Application Serial No.    PCT/US2011/029793, Kovatchev et al., entitled Method, System, and    Computer Program Product for Improving the Accuracy of Glucose    Sensors Using Insulin Delivery Observation in Diabetes,” filed Mar.    24, 2011.-   92. PCT/US2011/028163, Breton, et al., entitled “Method and System    for the Safety, Analysis and Supervision of Insulin Pump Action and    Other Modes of Insulin Delivery in Diabetes”, filed Mar. 11, 2011.-   93. Kovatchev B P and Breton M D. Method, system, and computer    program product for the detection of physical activity by changes in    heart rate, assessment of fast changing metabolic states, and    applications to closed and open control loop in diabetes.    PCT/US2007/085588; 2007.-   94. Kovatchev B P, Patek S D, Breton M D, and. System Coordinator    and Modular Architecture for Open-Loop and Closed-Loop Control for    Diabetes. PCT/US2010/036629, filed May 28, 2010.-   95. Bergman R N, Ider Y Z, Bowden C R, Cobelli C. Quantitative    estimation of insulin sensitivity. Am J Physiol. 236:E667-E677,    1979.-   The devices, systems, computer program products, and methods of    various embodiments of the invention disclosed herein may utilize    aspects disclosed in the following references, applications,    publications and patents and which are hereby incorporated by    reference herein in their entirety:-   A. International Patent Application Serial No. PCT/US2011/029793,    Kovatchev et al., entitled Method, System, and Computer Program    Product for Improving the Accuracy of Glucose Sensors Using Insulin    Delivery Observation in Diabetes,” filed Mar. 24, 2011-   B. PCT/US2011/028163, Breton, et al., entitled “Method and System    for the Safety, Analysis and Supervision of Insulin Pump Action and    Other Modes of Insulin Delivery in Diabetes”, filed Mar. 11, 2011.-   C. International Patent Application Serial No. PCT/US2010/047711,    Kovatchev, et al., “Tracking the Probability for Imminent    Hypoglycemia in Diabetes from Self-Monitoring Blood Glucose (SMBG)    Data”, filed Sep. 2, 2010.-   D. International Patent Application Serial No. PCT/US2010/047386,    Kovatchev, et al., “System, Method and Computer Program Product for    Adjustment of Insulin Delivery (AID) in Diabetes Using Nominal    Open-Loop Profiles”, filed Aug. 31, 2010.-   E. International Patent Application Serial No. PCT/US2010/040097,    Kovatchev, et al., “System, Method, and Computer Simulation    Environment for In Silico Trials in Prediabetes and Type 2    Diabetes”, filed Jun. 25, 2010.-   F. International Patent Application Serial No. PCT/US2010/036629,    Kovatchev, et al., “System Coordinator and Modular Architecture for    Open-Loop and Closed-Loop Control of Diabetes”, filed May 28, 2010    (Publication No. WO 2010/138848, Dec. 2, 2010).-   G. International Patent Application Serial No. PCT/US2010/025405,    Kovatchev, et al., entitled “Method, System and Computer Program    Product for CGM-Based Prevention of Hypoglycemia via Hypoglycemia    Risk Assessment and Smooth Reduction Insulin Delivery,” filed Feb.    25, 2010.-   H. International Patent Application Serial No. PCT/US2009/065725,    Kovatchev, et al., filed Nov. 24, 2009, entitled “Method, System,    and Computer Program Product for Tracking of Blood Glucose    Variability in Diabetes from Data.”-   I. International Patent Application Serial No. PCT/US2008/082063,    Magni, et al., entitled “Model Predictive Control Based Method for    Closed-Loop Control of Insulin Delivery in Diabetes Using Continuous    Glucose Sensing”, filed Oct. 31, 2008; U.S. patent application Ser.    No. 12/740,275, Magni, et al., entitled “Predictive Control Based    System and Method for Control of Insulin Delivery in Diabetes Using    Glucose Sensing”, filed Apr. 28, 2010.-   J. International Patent Application Serial No. PCT/US2008/069416,    Breton, et al., entitled “Method, System and Computer Program    Product for Evaluation of Insulin Sensitivity, Insulin/Carbohydrate    Ratio, and Insulin Correction Factors in Diabetes from    Self-Monitoring Data”, filed Jul. 8, 2008, (Publication No. WO    2009/009528, Jan. 15, 2009); U.S. patent application Ser. No.    12/665,149, Breton, et al., “Method, System and Computer Program    Product for Evaluation of Insulin Sensitivity, Insulin/Carbohydrate    Ratio, and Insulin Correction Factors in Diabetes from    Self-Monitoring Data”, filed Dec. 17, 2009.-   K. International Patent Application Serial No. PCT/US2008/067725,    Kovatchev, et al., entitled “Method, System and Computer Simulation    Environment for Testing of Monitoring and Control Strategies in    Diabetes,” filed Jun. 20, 2008, (Publication No. WO 2008/157781,    Dec. 24, 2008); U.S. patent application Publication Ser. No.    12/664,444, Kovatchev, et al., filed Dec. 14, 2009, entitled    “Method, System and Computer Simulation Environment for Testing of    Monitoring and Control Strategies in Diabetes”, (Publication No.    2010/0-179768, Jul. 15, 2010).-   L. International Patent Application Serial No. PCT/US2008/067723,    Patek, et al., entitled “LQG Artificial Pancreas Control System and    Related Method”, filed on Jun. 20, 2008.-   M. U.S. patent application Ser. No. 12/516,044, Kovatchev, et al.,    filed May 22, 2009, entitled “Method, System, and Computer Program    Product for the Detection of Physical Activity by Changes in Heart    Rate, Assessment of Fast Changing Metabolic States, and Applications    of Closed and Open Control Loop in Diabetes”.-   N. International Patent Application Serial No. PCT/US2007/085588,    Kovatchev, et al., filed Nov. 27, 2007, entitled “Method, System,    and Computer Program Product for the Detection of Physical Activity    by Changes in Heart Rate, Assessment of Fast Changing Metabolic    States, and Applications of Closed and Open Control Loop in    Diabetes”, (Publication No. WO2008/067284, Jun. 5, 2008)-   O. U.S. patent application Ser. No. 11/943,226, Kovatchev, et al.,    filed Nov. 20, 2007, entitled “Systems, Methods and Computer Program    Codes for Recognition of Patterns of Hyperglycemia and Hypoglycemia,    Increased Glucose Variability, and Ineffective Self-Monitoring in    Diabetes”.-   P. U.S. patent application Ser. No. 11/578,831, Kovatchev, et al.,    filed Oct. 18, 2006 entitled “Method, System and Computer Program    Product for Evaluating the Accuracy of Blood Glucose Monitoring    Sensors/Devices”, (Publication No. US2007/0232878, Oct. 4, 2007),    U.S. Pat. No. 7,815,569, Kovatchev, et al., issued Oct. 29, 2010-   Q. International Application Serial No. PCT/US2005/013792,    Kovatchev, et al., filed Apr. 21, 2005, entitled “Method, System,    and Computer Program Product for Evaluation of the Accuracy of Blood    Glucose Monitoring Sensors/Devices”, (Publication No. WO 05106017,    Nov. 10, 2005-   R. International Patent Application Serial No. PCT/US01/09884,    Kovatchev, et al., filed Mar. 29, 2001, entitled “Method, System,    and Computer Program Product for Evaluation of Glycemic Control in    Diabetes Self-Monitoring Data”, (Publication No. WO 01/72208, Oct.    4, 2001).-   S. U.S. patent application Ser. No. 10/240,228, Kovatchev, et al.,    filed Sep. 26, 2002, (Publication No. 0212317, Nov. 13, 2003), U.S.    Pat. No. 7,025,425 B2, Kovatchev, et al., issued Apr. 11, 2006,    entitled “Method, System, and Computer Program Product for the    Evaluation of Glycemic Control in Diabetes from Self-Monitoring    Data”.-   T. U.S. patent application Ser. No. 11/305,946, Kovatchev, et al.,    filed Dec. 19, 2005 entitled “Method, System, and Computer Program    Product for the Evaluation of Glycemic Control in Diabetes from    Self-Monitoring Data” (Publication No. 2006/0094947, May 4, 2006),    U.S. Pat. No. 7,874,985, Kovatchev, et al., issued Jan. 25, 2011.-   U. U.S. patent application Ser. No. 12/975,580, Kovatchev, et al.,    “Method, System, and Computer Program Product for the Evaluation of    Glycemic Control in Diabetes from Self-Monitoring Data”, filed Dec.    22, 2010.-   V. International Patent Application Serial No. PCT/US2003/025053,    Kovatchev, et al., filed Aug. 8, 2003, entitled “Method, System, and    Computer Program Product for the Processing of Self-Monitoring Blood    Glucose (SMBG) Data to Enhance Diabetic Self-Management”,    (Publication No. WO 2004/015539, Feb. 19, 2004).-   W. U.S. patent application Ser. No. 10/524,094, Kovatchev, et al.,    filed Feb. 9, 2005 entitled “Managing and Processing Self-Monitoring    Blood Glucose” (Publication No. 2005/214892, Sep. 29, 2005).-   X. U.S. patent application Ser. No. 12/065,257, Kovatchev, et al.,    filed Aug. 29, 2008, entitled “Accuracy of Continuous Glucose    Sensors”, (Publication No. 2008/0314395, Dec. 25, 2008).-   Y. International Patent Application Serial No PCT/US2006/033724,    Kovatchev, et al., filed Aug. 29, 2006, entitled “Method for    Improvising Accuracy of Continuous Glucose Sensors and a Continuous    Glucose Sensor Using the Same”, (Publication No. WO 07027691, Mar.    8, 2007).-   Z. U.S. patent application Ser. No. 12/159,891, Kovatchev, B., filed    Jul. 2, 2008, entitled “Method, System and Computer Program Product    for Evaluation of Blood Glucose Variability in Diabetes from    Self-Monitoring Data”, (Publication No. 2009/0171589, Jul. 2, 2009).-   AA. International Application No. PCT/US2007/000370, Kovatchev, B.,    filed Jan. 5, 2007, entitled “Method, System and Computer Program    Product for Evaluation of Blood Glucose Variability in Diabetes from    Self-Monitoring Data”, (Publication No. WO 07081853, Jul. 19, 2007).-   BB. U.S. patent application Ser. No. 11/925,689 and PCT    International Patent Application No. PCT/US2007/082744, Breton, et    al., both filed Oct. 26, 2007, entitled “For Method, System and    Computer Program Product for Real-Time Detection of Sensitivity    Decline in Analyte Sensors”, (Publication Nos. 2008/0172205, Jul.    17, 2008 and WO 2008/052199, May 2, 2008).-   CC. U.S. patent application Ser. No. 10/069,674, Kovatchev, et al.,    filed Feb. 22, 2002, entitled “Method and Apparatus for Predicting    the Risk of Hypoglycemia”.-   DD. International Application No. PCT/US00/22886, Kovatchev, et al.,    filed Aug. 21, 2000, entitled “Method and Apparatus for Predicting    the Risk of Hypoglycemia”, (Publication No. WO 01/13786, Mar. 1,    2001).-   EE. U.S. Pat. No. 6,923,763 B1, Kovatchev, et al., issued Aug. 2,    2005, entitled “Method and Apparatus for Predicting the Risk of    Hypoglycemia”.-   FF. U.S. Patent Application Publication No. US 2004/0254434 A1,    “Glucose Measuring Module and “Insulin Pump Combination”, published    Dec. 16, 2004., Goodnow, et al. Ser. No. 10/458,914, filed Jun. 10,    2003.-   GG. U.S. Patent Application Publication No. US 2009/00697456 A1,    Estes, et al., “Operating an Infusion Pump System”, published Mar.    12, 2009. Ser. No. 11/851,194, Sep. 6, 2007.-   HH. Fernandez-Luque, et al., eDiab: A System for Monitoring,    Assisting and Educating People with Diabetes”, ICCHP 2006, LNCS    4061, pp. 1342-1349, 2006.-   II. U.S. Pat. No. 6,602,191 B2, Quy, R., Method and Apparatus for    Health and Disease Management Combining Patient Data Monitoring with    Wireless Internet Connectivity, Aug. 5, 2003.-   JJ. International Patent Application Publication No. WO 2008/064053    A2, Patel, et al., Systems and Methods for Diabetes Management Using    Consumer Electronic Devices, May 29, 2008; International Patent    Application Serial No. PCT/US2007/084769, filed Nov. 15, 2007.-   KK. International Patent Application Publication No. WO 2010/138817    A1, Ow-Wing, K., Glucose Monitoring System with Wireless    Communications, Dec. 2, 2010; International Patent Application    Serial No. WO 2010/138817 A1, filed May 28, 2010.-   LL. International Patent Application Publication No. WO 2004/052204    A1, Kim, Kwan-Ho, Blood Glucose Monitoring System, Jun. 24, 2004;    International Patent Application Serial No. PCT/KR2003/000398, filed    Feb. 28, 2003.

What is claimed is:
 1. A system for treating a patient with insulin, thepatient suffering from diabetes mellitus (DM), by managing glycemiccontrol of the patient, the system comprising a computer processor thatincludes: an input module configured to accept input data from one ormore of a plurality of diverse blood glucose measurement devices, one ormore of a plurality of diverse insulin delivery devices, and otherphysiological data of the patient; a data classifier module configuredto classify data accepted by said input module based on frequency,dimensionality, and quality of the accepted data; a patient stateestimation module configured to process input data in accordance with atleast one data processing algorithm corresponding to the classificationof the input data as determined by the data classifier module; a patientrisk status module configured to determine a level of risk of saidpatient with respect to one or more glycemic states using processed datafrom said patient state estimation module; and an output moduleconfigured to output control signals for said blood glucose measurementdevices and said insulin delivery devices based on the level of riskdetermined by said patient risk status module.
 2. A system as set forthin claim 1, wherein said diverse blood glucose measurement devicescomprise a self-monitoring blood glucose (SMBG) device and a continuousglucose monitoring (CGM) device.
 3. A system as set forth in claim 1,wherein said diverse insulin delivery devices comprise a multiple dailyinsulin injection pump and a continuous subcutaneous insulin injectionpump.
 4. A system as set forth in claim 3, further comprising a safetyservice module configured to analyze data from at least one of saidinsulin injection pumps and to determine an operational state of saidpump from said data analysis.
 5. A system as set forth in claim 4,wherein said safety service module is further configured to screeninsulin bolus commands for safety prior to said commands being deliveredto an insulin injection pump.
 6. A system as set forth in claim 1,wherein said input data is classified as one of SMBG data, SMBG plusinsulin pump data, CGM data, or CGM plus insulin pump data.
 7. A systemas set forth in claim 1, wherein said input module is further configuredto receive data from a heart rate sensor.
 8. A system as set forth inclaim 1, wherein said input module is further configured to receive datafrom a blood pressure sensor.
 9. A system as set forth in claim 1,wherein said input module is further configured to receive data from anaccelerometer sensor.
 10. A system as set forth in claim 1, wherein saidinput module is further configured to receive data from an ECG/EKGsensor.
 11. A system as set forth in claim 1, wherein said input moduleis further configured to receive data from a heart rate sensor.
 12. Asystem as set forth in claim 1, wherein said input module is furtherconfigured to receive data from a plurality of different physiologicalsensors for said patient, said plurality of sensors forming a bodysensor network.
 13. A system as set forth in claim 1, further comprisinga telecommunications module configured to communicate patient statusdata to a remote health care provider.
 14. A system as set forth inclaim 1, further comprising a telecommunications module configured tocommunicate patient status data to an emergency responder.
 15. A systemas set forth in claim 1, further comprising a telecommunications moduleconfigured to communicate patient status data to a public health careorganization.
 16. A system as set forth in claim 1, further comprising adiabetes management or control application module downloaded from athird party source.
 17. A system as set forth in claim 1, wherein saidsystem is implemented on a cell phone.
 18. A system as set forth inclaim 1, wherein said system is implemented on a tablet computer.
 19. Asystem as set forth in claim 1, wherein said control signals areconfigured to cause monitoring of said patient to be modified from acurrent level of monitoring to a higher level of monitoring independence on a determined risk status of said patient and adetermination of data quality and/or density.
 20. A system as set forthin claim 1, wherein said control signals are configured to carry outclosed-loop control of insulin delivery to said patient in dependence ona determined risk status of said patient.
 21. The system of claim 1wherein the output module is further configured to output advisorymessages that include insulin dosage information and patient alerts. 22.A non-transitory computer-readable storage medium having stored thereoncomputer-executable instructions for treating a patient with insulin,the patient suffering from diabetes mellitus (DM), by managing glycemiccontrol of the patient, comprising instructions for: accepting inputdata from one or more of a plurality of diverse blood glucosemeasurement devices, one or more of a plurality of diverse insulindelivery devices, and other physiological data of the patient;classifying the accepted input data based on frequency, dimensionality,and quality of the accepted data; processing the classified input datain accordance with at least one data processing algorithm correspondingto the classifying; determining a level of risk of said patient withrespect to one or more glycemic states using the processed data; andoutputting signals that control said blood glucose measurement devicesand said insulin delivery devices based on the determined level of risk.23. A non-transitory computer-readable storage medium as set forth inclaim 22, wherein said diverse blood glucose measurement devicescomprise a self-monitoring blood glucose (SMBG) device and a continuousglucose monitoring (CGM) device.
 24. A non-transitory computer-readablestorage medium as set forth in claim 22, wherein said diverse insulindelivery devices comprise a multiple daily insulin injection pump and acontinuous subcutaneous insulin injection pump.
 25. A non-transitorycomputer-readable storage medium as set forth in claim 24, furthercomprising instructions for analyzing data from at least one of saidinsulin injection pumps and to determine an operational state of saidpump from said data analysis.
 26. A non-transitory computer-readablestorage medium as set forth in claim 25, further comprising instructionsfor screening insulin bolus commands for safety prior to said commandsbeing delivered to an insulin injection pump.
 27. A non-transitorycomputer-readable storage medium as set forth in claim 22, wherein saidinput data is classified as one of SMBG data, SMBG plus insulin pumpdata, CGM data, or CGM plus insulin pump data.
 28. A non-transitorycomputer-readable storage medium as set forth in claim 22, furthercomprising instructions for receiving data from a heart rate sensor. 29.A non-transitory computer-readable storage medium as set forth in claim22, further comprising instructions for receiving data from a bloodpressure sensor.
 30. A non-transitory computer-readable storage mediumas set forth in claim 22, further comprising instructions for receivingdata from an accelerometer sensor.
 31. A non-transitorycomputer-readable storage medium as set forth in claim 22, furthercomprising instructions for receiving data from an ECG/EKG sensor.
 32. Anon-transitory computer-readable storage medium as set forth in claim22, further comprising instructions for receiving data from a heart ratesensor.
 33. A non-transitory computer-readable storage medium as setforth in claim 22, further comprising instructions for receiving datafrom a plurality of different physiological sensors for said patient,said plurality of sensors forming a body sensor network.
 34. Anon-transitory computer-readable storage medium as set forth in claim22, further comprising instructions for communicating patient statusdata to a remote health care provider over a telecommunications network.35. A non-transitory computer-readable storage medium as set forth inclaim 22, further comprising instructions for communicating patientstatus data to an emergency responder over a telecommunications network.36. A non-transitory computer-readable storage medium as set forth inclaim 22, further comprising instructions for communicating patientstatus data to a public health care organization over atelecommunications network.
 37. A non-transitory computer-readablestorage medium as set forth in claim 22, further comprising diabetesmanagement or control application instructions provided by a third partysource.
 38. A non-transitory computer-readable storage medium as setforth in claim 22, wherein said medium is installed on a cell phone. 39.A non-transitory computer-readable storage medium as set forth in claim22, wherein said medium is installed on a tablet computer.
 40. Anon-transitory computer-readable storage medium as set forth in claim22, wherein said control signals are configured to cause monitoring ofsaid patient to be modified from a current level of monitoring to ahigher level of monitoring in dependence on a determined risk status ofsaid patient and a determination of data quality and/or density.
 41. Anon-transitory computer-readable storage medium as set forth in claim22, wherein said control signals are configured to carry out closed-loopcontrol of insulin delivery to said patient in dependence on adetermined risk status of said patient.
 42. The non-transitorycomputer-readable storage medium of claim 22 further comprisinginstructions for outputting advisory messages that include insulindosage information and patient alerts.
 43. A system for treating apatient with insulin, the patient suffering from diabetes mellitus (DM),by managing glycemic control of the patient, the system comprising acomputer processor configured to: accept input data from one or more ofa plurality of diverse blood glucose measurement devices, one or more ofa plurality of diverse insulin delivery devices, and other physiologicaldata of the patient; classify the accepted data based on frequency,dimensionality, and quality of the accepted data; process the classifieddata in accordance with at least one data processing algorithmcorresponding to the classification; determine a level of risk of saidpatient with respect to one or more glycemic states using the processeddata; and control said blood glucose measurement devices and saidinsulin delivery devices based on the determined level of risk.
 44. Thesystem of claim 43 comprising a computer processor further configured tooutput advisory messages that include insulin dosage information andpatient alerts.